Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study

Inflammation is associated with a range of neuropsychiatric symptoms; however, the nature of the causal relationship is unclear. We used complementary non-genetic, genetic risk score (GRS), and Mendelian randomization (MR) analyses to examine whether inflammatory markers are associated with affect, depressive and anxiety disorders, and cognition. We tested in ≈ 55,098 (59% female) individuals from the Dutch Lifelines cohort the concurrent/prospective associations of C-reactive protein (CRP) with: depressive and anxiety disorders; positive/negative affect; and attention, psychomotor speed, episodic memory, and executive functioning. Additionally, we examined the association between inflammatory GRSs (CRP, interleukin-6 [IL-6], IL-6 receptor [IL-6R and soluble IL-6R (sIL-6R)], glycoprotein acetyls [GlycA]) on these same outcomes (Nmax=57,946), followed by MR analysis examining evidence of causality of CRP on outcomes (Nmax=23,268). In non-genetic analyses, higher CRP was associated with a depressive disorder, lower positive/higher negative affect, and worse executive function, attention, and psychomotor speed after adjusting for potential confounders. In genetic analyses, CRPgrs was associated with any anxiety disorder (β = 0.002, p = 0.037) whereas GlycAGRS was associated with major depressive disorder (β = 0.001, p = 0.036). Both CRPgrs (β = 0.006, p = 0.035) and GlycAGRS (β = 0.006, p = 0.049) were associated with greater negative affect. Inflammatory GRSs were not associated with cognition, except slL-6RGRS which was associated with poorer memory (β=−0.009, p = 0.018). There was weak evidence for a CRP-anxiety association using MR (β = 0.12; p = 0.054). Genetic and non-genetic analyses provide consistent evidence for an association between CRP and negative affect. These results suggest that dysregulated immune physiology may impact a broad range of trans-diagnostic affective symptoms.

Inferring causality from observational studies is a challenge due to confounding (e.g., stress, poor sleep 22 ) and reverse causality (i.e., whether in ammation impacts depression/cognition, or vice versa).Mendelian randomization (MR) is a genetic epidemiological method that can test causal relationships by using genetic variants associated with an exposure (e.g., in ammation) as proxies for the exposure 33 .As genetic variants are randomly inherited from parents to offspring and are xed at conception, they are less likely to be associated with confounders and overcome issues of reverse causation 33 .Preliminary evidence, using MR, implicate IL-6 and its soluble IL-6 receptor (sIL-6R) in depression [34][35][36][37] .To date, most MR studies examining the effect of IL-6 on health have focused on circulating IL-6 levels.However, IL-6 signals via multiple pathways (trans-signaling, classical-signaling, and trans-presentation) and there is growing evidence that IL-6 trans-signaling is primarily responsible for the pathogenic in ammatory effects of IL-6 38 .Here, we include variants related to circulating IL-6 levels, and sIL-6R levels (relevant for IL-6 trans-signaling).Causal evidence for CRP and other proin ammatory markers [i.e., Glycoprotein Acetyls (GlycA) a composite biomarker thought to provide a more stable marker of in ammation which re ects the glycosylation of multiple acute-phase proteins 39 ] on depression are mixed 34,37,[40][41][42][43] .Regarding cognition, few studies have examined potential causal relationships with in ammation.MR analyses using available genome-wide association studies (GWAS) report both null results of in ammatory biomarkers on emotion recognition, working memory, response inhibition 44 as well as effects of speci c cytokines/chemokines (i.e., Eotaxin, IL-8) on uid intelligence 45 .
The current study used data from the Lifelines Cohort Study -a large population-based cohort in the Netherlands -to conduct complementary non-genetic and genetic analysis to investigative the causal relationship between in ammation and negative affect, depressive disorders, and cognitive task performance.First, we used cross-sectional and longitudinal non-genetic analysis examine the association between circulating levels of CRP and depression/cognitive performance.Second, we conducted genetic risk score (GRS) and MR analysis to test whether genetic variants regulating levels and activity of CRP, IL-6, and GlycA were causally related with depression/cognitive performance.We also conducted the above analyses on closely related constructs (e.g., anxiety, negative/positive affect), for which associations with in ammation have previously been observed 46-51 but for which considerably less empirical data has been published.We hypothesized that both circulating CRP levels and genetically predicted in ammatory biomarkers (i.e., CRP, IL-6, sIL-6R, and GlycA) would be associated with depression, cognitive task performance, affect, and anxiety.

Participants
Lifelines is a multi-disciplinary prospective population-based cohort study examining in a unique three-generation design the health and health-related behaviors of 167,729 persons living in the North of the Netherlands.It employs a broad range of investigative procedures in assessing the biomedical, socio-demographic, behavioral, physical and psychological factors which contribute to the health and disease of the general population, with a special focus on multi-morbidity and complex genetics 52 .This cohort has previously been described in detail 52 .In brief, participants were recruited via their general practitioner (49%), participating family members (38%), and self-registration on the Lifelines website (13%).Exclusion criteria for recruitment through the general practitioner included: insu cient knowledge of Dutch language, severe psychiatric or physical illness, limited life expectancy (< 5 years).Baseline data included approximately: 140,000 adults (18-65 years), 15,000 children (0-17 years), 12,000 elderly individuals (65 + years).Following baseline assessment, participants are invited to complete an in-person study visit every 5 years.Phenotypic and genotypic data are collected by Lifelines to permit investigation on determinants of health.Data for the current study were drawn from 147,815 individuals who were aged 18 + years at baseline and who did not report a diagnosis that typically impairs cognitive function, speci cally Alzheimer's disease, other dementias, epilepsy, multiple sclerosis, Parkinson's disease, and stroke.In the non-genetic analyses, the analytic sample is smaller as CRP was assessed in a sub-sample of individuals (N = 55,098) as was baseline cognitive performance on the Ruff Figural Fluency Test (N = 88,096).The analytic sample is smaller for non-genetic (N ≤ 55,098), GRS (N ≤ 57,946) and MR (N ≤ 23,268) analysis as only a subset of participants who met inclusion criteria had outcome data and CRP data (non-genetic analysis), genetic data (GRS), or genetic and CRP data (MR) due to time and cost constraints.Phenotypic data were drawn from both the baseline assessment and the rst follow-up assessment; whether a speci c measure was assessed at baseline, rst follow-up or both assessments is noted for each measure.

Measures Cognition
Ruff Figural Fluency Test (baseline assessment).The Ruff Figural Fluency Test (RFFT) is a reliable and valid measure of gural uency, a dimension of executive function 53 .Participants were asked to draw as many unique designs as possible within 60 seconds by connecting dots in different patterns.The task is composed of ve parts, with each part containing 35 identical ve-dot patterns (with or without distractors).The total number of unique designs was used as the dependent variable in the analyses, consistent with previous studies 54 .In Lifelines, the RFFT was administered to all participants until April 2012, and subsequently in a random half of the sample.Data from participants who failed to generate a single unique design per trial (n = 181) were deemed invalid and removed.
Cogstate Test Battery ( rst follow-up assessment).The Cogstate Test Battery took approximately 10-15 minutes to complete and consisted of four tasks: detection task (psychomotor speed), identi cation task (attention), one-back task (working memory), and one card learning task (episodic memory).For each task, outcomes recommended by Cogstate were selected, speci cally: log10 transformed response time in millisecond (detection/identi cation tasks) and arcsine-transformed response accuracy (one-back/one card learning tasks).For the detection and identi cation tasks, higher values re ect poorer performance and for the one-back and one card learning tasks, higher values equal better performance.Data cleaning involved excluding participants with a high number of errors.The percentage of successful trials per Cogstate task was high, averaging 66% (n = 85,050; SD = .11)on the episodic memory task, 91% (n = 85,053; SD = .17)on the visual attention task, 92% (n = 85,053; SD = .20)on the psychomotor speed task, and 90% (n = 85,051; SD = .15)on the working memory task.A small number of participants exhibiting implausibly low accuracy rates indicative of poor effort, failure to comprehend task instructions, or technical errors were excluded from analyses.Speci cally individuals with an accuracy rate less than: 25% on the episodic memory task (n = 231), 40% on the visual attention task (n = 2,878), 45% on the psychomotor speed task (n = 3,914), and 35% on the working memory task (n = 1,330).For more details on the Cogstate Test Battery, see Supplementary Methods 1.1.

Clinical Assessments
Anxiety and Depressive Disorders (baseline/ rst follow-up assessments).The Mini International Neuropsychiatric Interview -Simpli ed (MINI) is a reliable, valid, and brief structured interview that was designed to screen for psychiatric disorders [55][56][57] .Lifelines used an adapted version of a Dutch translation of the MINI that was administered by trained interviewers at baseline and self-administered on location at follow-up -details on the version used in Lifelines have previously been published 58 .Participants were considered to meet criteria for any depressive disorder if they met the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV criteria for Major Depressive Disorder (MDD) or dysthymia at the time of the interview.Impairment was assessed in the MINI for dysthymia but not depression and consequently, impairment was not used as a criterion for MDD.Any anxiety disorder refers to meeting current criteria for any one of the following conditions that was assessed using the MINI: panic disorder, agoraphobia, social phobia, or Generalized Anxiety Disorder (GAD).
We used four diagnostic groups as outcome variables: MDD, any depressive disorder, GAD, and any anxiety disorder.
Positive and Negative Affect Schedule (baseline assessment).The Positive and Negative Affect Schedule (PANAS) assesses positive and negative affect 59 using two 10-item subscales (examples of items include 'excited' on positive subscale; 'upset' on negative subscale).Participants are asked to rate the extent that they experienced each item during the last four weeks on a ve-point scale (ranging from 'not at all' to 'extremely').The outcome is the summed score on each subscale, which ranges from 10 to 50 (higher value re ects higher positive or negative affect, respectively).The PANAS has been shown to be reliable and valid 60 .

C-reactive Protein (baseline assessment)
Participants gave blood samples before 10AM via venipuncture following an overnight fast.Complete details on blood specimen data collection have previously been reported 52,54 .Due to assay costs, CRP was assessed in approximately 35% of Lifelines participants and data were available for 55,098 individuals in the analytic sample.CRP was quanti ed using three separate methods over the course of baseline assessment (Method 1: 12.90% of total CRP values assessed in serum; CardioPhase hsCRP; Method 2: 84.58% of total CRP values, assessed in plasma; CardioPhase high sensitivity (hs)CRP, Siemens Healthcare Diagnostics, Marburg, Germany; Method 3: 2.52% of total CRP values; assessed in plasma; CRPL3, Roche Diagnostics, Mannheim, Germany).
Assay methods 2 and 3 were identical and only differed in terms of the manufacturer.A conversion formula (new = 0.92 x old − 0.01) was derived from an internal validation using 39 samples, according to the AMC (alternative method comparison, Deming Regression) protocol in order that Method 1 could be compared with Method 2 and 3 54 .For CardioPhase hsCRP, the intra-assay coe cient of variability was 3.45% and the inter-assay coe cient of variability was 3.15%.For CRPL3, the intra-assay coe cient of variability was 4.15% and the inter-assay coe cient of variability was 5.8%.

Genetic Data
Genotype data were available for a subgroup of participants in Lifelines.Genotyping was conducted using three chip arrays: Illumina CytoSNP-12 Bead Chip v2 array (N = 17,033), In nium Global Screening Array (GSA) Beadchip-24 v1.0 (N = 38,030), FinnGen Thermo Fisher Axiom ® custom array (Affymetrix; N = 29,166).For details on quality checks (QC's) and imputation conducted by Lifelines, see Supplementary Methods 1.2.Following Lifelines QC's, the total sample size for participants who met criteria for this study: CytoSNP (N = 14,942), GSA (N = 31,810) and Affymetrix (N = 26,334).We applied additional QC's which included removing: one of the duplicates (individuals who were genotyped on more than one chip) and rst-degree relatives between chips, non-European individuals (identi ed by Lifelines), and genetic outliers (identi ed by Lifelines); see Supplementary Fig. 1 for more details.This resulted in a total of 58,713 participants with genetics data included in this study (CytoSNP N = 7,632; GSA N = 24,975; Affymetrix N = 26,106).For more details on the genetic data in Lifelines, see Supplementary Methods 1.2.

Covariates
Covariates included age, sex, educational attainment, body mass index (BMI) and health status.Age, sex, and educational attainment were self-reported by participants.Educational attainment was determined using a single-item question and was categorized by Lifelines as: low [no education, primary education, lower/preparatory vocational education, lower general secondary education (leaving secondary school aged > 16 years)], moderate (intermediate vocational education/apprenticeship, higher secondary education), and high (higher vocational education, university).We recoded educational attainment so that higher values represent lower educational attainment.To estimate body mass index (BMI), height was measured to the closest 0.1 cm and body weight was measured without shoes to 0.1 kg precision.For health status, a composite measure was created counting several self-reported chronic medical conditions related to increased levels of in ammation (i.e., arthritis, asthma, coeliac disease, Crohn's disease, diabetes, and psoriasis); we then categorized participants into those with no relevant chronic medical condition, 1, 2 or 3 + conditions.

Analyses
Analyses were conducted in R version 4.1.1.

Non-genetic Analyses
Multivariable linear and logistic regression models were estimated using base functions in R (i.e., 'lm', 'glm').CRP was transformed by natural log to impose a normal distribution.

Genetic Risk Scores
Genetic risk scores (GRS) were calculated to determine whether GRS for in ammatory markers (CRP, IL-6, IL-6R, sIL-6R, GlycA) were associated with depression/anxiety, affect and cognitive outcomes.To create GRS for each in ammatory marker, we identi ed genetic variants (single nucleotide polymorphisms [SNPs]) associated with these proteins in large available GWAS or using SNP lists from previous publications, see Supplementary Table 1.
For details on the GWAS used and accessing summary statistics, please see Supplementary Methods 1.3 and Supplementary Tables 2-3.The following criteria were used to identify SNPs from GWAS for each in ammatory marker: p-value < 5x10 − 8 , linkage disequilibrium clumping (r 2 = 0.01, kb = 1000 based on the European-clustering individuals in the 1000 genomes reference panel) using ld_clump() 61 in the ieugwasr package, minor allele frequency > 0.01.In the primary analysis, we restricted the SNP set to cis variants (SNPs+/-1Mb from protein coding gene based on Genome Reference Consortium Human Build used in the GWAS) [62][63][64] .The reason for restricting to cis variants in the primary analysis is because, due to their proximity to the protein coding gene, they are more likely to be valid instruments, as they are more likely to in uence mRNA expression and protein levels (thus being less pleiotropic) 65 .For GlycA, which does not have a single coding gene due to its composite nature, we used the largest available GWAS in our primary analysis.In our secondary analyses, we used both cis and trans variants from GWAS (i.e., we did not restrict to cis variants).Each SNP list was used to create a weighted GRS for each Lifelines participant.Speci cally, the risk alleles were weighted by the effect size (beta) reported in the GWAS/previous study and then summed to provide a risk score.Any SNP identi ed in GWAS/previous study that was not available in Lifelines was replaced with a proxy (where possible) that had r 2 > 0.8 (using LDproxy_batch function in EUR population in R), rsID (SNP name) available, SNP available in full summary statistic GWAS, and in Lifelines 66, 67 .GRS were created in Plink v1.90 68 and continuous phenotypes were standardized within each chip (z-scored) for direct comparison (CRP levels were log transformed but not standardized).
To adjust for relatedness within each chip, two approaches were taken.The primary approach applied the GRAMMAR method 69 and the secondary approach involved re-running analyses removing close relatives (up to rst-degree, up to second-degree, and up to third-degree), see Supplementary Materials 1.4 and 1.5 for details.We then ran regression models predicting each outcome using the standardized GRS, including top 10 genetic PCs (calculated on merged Lifelines genotype data), age, sex, and chip.Maximum sample size for analyses: no relatives within chips removed (N = 58,713), up to rst-degree removed (N = 50,955), up to second-degree removed (N = 50,255), up to third-degree removed (N = 48,880).Unadjusted analyses are also reported in the Supplementary Tables 6 and 7.

Mendelian randomization
To conduct MR using individual level data and two-stage least squares regression, genetic data, exposure data, and outcome data are required.As only CRP is available within the Lifelines cohort (IL-6 and GlycA are not currently available), only this in ammatory marker could be assessed in the MR analysis.Where there was evidence of associations between CRP GRS and outcomes, we followed this up with MR to assess potential causality.Two-stage least squares regressions were conducted using the AER package 70 .Analyses were GRAMMAR adjusted for relatedness and all regression models adjusted for age, sex, and chip.To check MR assumptions, we ran linear regressions to test whether CRP GRS were associated with circulating CRP levels in participants with both genetic and CRP data available (n = 23,607) using the GRAMMAR method.We also checked whether any in ammatory marker GRS were associated with potential confounders (BMI, current smoking status, educational attainment; all models were adjusted for age, sex, and chip).

Results
The characteristics of the Lifelines cohort sample are described in Table 1 and Pearson correlations between study variables are presented in Table 2. Association of CRP with affect, depressive and anxiety disorders, and cognition The association of (log-transformed) CRP with: clinical outcomes (i.e., MDD, any depressive disorder, GAD, any anxiety disorder), positive and negative affect, and ve cognitive measures [RFFT (executive functioning), detection task (psychomotor speed), identi cation task (attention), one-back task (working memory), and one card learning task (episodic memory)] are illustrated in Table 3, both unadjusted and adjusted for covariates.Notably, CRP was associated with a greater likelihood of meeting criteria for a range of clinical outcomes, with a numerically greater likelihood consistently reported for depression as compared to anxiety at baseline and rst follow-up assessment.However, these associations were attenuated after controlling for confounding by age, sex, education, health status, and BMI.Higher CRP was also associated with higher negative affect, lower positive affect, and worse cognitive task performance, although the magnitude of associations was generally very small and negligible after controlling for covariates.

Associations of GRSs for in ammatory markers with affect, depressive and anxiety disorders
In the primary analysis, CRP GRS (cis) was associated with a higher negative affect score (beta: 0.006; 95% CI: 0.0005 to 0.012, p = 0.035, N = 57,946) and increased risk of any anxiety disorder (beta: 0.002, 95% CI: 0.0001 to 0.004, p = 0.037, N = 57,047).GlycA GRS was associated with higher negative affect score (beta: 0.006, 95% CI: 0.00002 to 0.012, p = 0.049; N = 57,946) and increased risk of MDD (beta: 0.001, 95% CI: 0.0001 to 0.002; p = 0.036; N = 57,047).Other in ammatory marker GRSs were not associated with depressive/anxiety disorders or affect scores (ps ≥ 0.15).In the secondary analysis, there was evidence that CRP GRS (genome-wide) was associated with increased risk of any anxiety disorders (beta: 0.002, 95% CI: 0.0003 to 0.004, p = 0.023, N = 57,047).There was little evidence that other in ammatory marker GRSs were associated with depressive/anxiety disorders or affect (ps ≥ 0.16).For all results, see Fig. 1 and Supplementary Table 5.All sensitivity analyses removing differing degrees of related individuals (up to 1st -degree, up to 2nd -degree, up to 3rd -degree) within chips (non-GRAMMAR method) did not substantially alter results, see Supplementary Tables 8-13.Although, there was slightly stronger evidence for associations between GlycA GRS and negative affect score (ps ≤ 0.015) and between CRP GRS (cis) and negative affect score (ps ≤ 0.033).

Association of GRS for in ammatory markers and cognition
In primary analyses, in ammatory marker GRSs were not associated with performance on cognitive tasks (ps ≥ 0.14), except for sIL-6R GRS which was negatively associated with episodic memory performance (one card learning task accuracy; beta: -0.009, 95% CI: -0.017 to -0.002, p = 0.018, N = 36,783).In secondary analyses, in ammatory markers GRSs (genome-wide) were not associated with performance on cognitive tasks (ps ≥ 0.22).For all results, see Fig. 2 and Supplementary Table 5. Sensitivity analyses after removing related individuals within chips (non-GRAMMAR method) did not alter the results, see Supplementary Tables 11-13.
Testing potential causality between CRP, negative affect, and anxiety disorders using Mendelian randomization with individual level data CRP genetic instruments had F-statistics > 10 (158 for cis GRS, 1045 for genome-wide GRS), indicating adequate instrument strength 71 .For tests on the MR assumptions, see Supplementary Results 2.2.There was weak evidence that genetically-proxied CRP (cis) causally increased the risk of any anxiety disorders (beta: 0.12, p = 0.054, N = 22,154), and little evidence on negative affect (beta: 0.27, p = 0.16; N = 23,268).Sensitivity analysis removing related individuals did not alter overall conclusions.
The overall pattern of results for the non-genetic and genetic analyses are visualized in Fig. 3.

Discussion
We conducted complementary non-genetic and genetic analyses to interrogate the relationship between in ammatory markers and affect, depressive and anxiety disorders, and cognitive task performance using data from the Lifelines cohort.In non-genetic analyses, higher CRP was associated with diagnosis of any depressive disorder, positive and negative affect scores, gural uency, attention, and psychomotor speed after adjusting for potential confounders, although the magnitude of these associations was generally small.In genetic analyses, genetic risk scores for CRP (CRP GRS ) and GlycA GRS both associated with higher negative affect score.CRP GRS was associated with any anxiety disorder whereas GlycA GRS was associated with major depressive disorder.In ammatory marker GRSs were not associated with cognitive task performance, except for soluble IL-6R GRS which was associated with poorer memory Individual level MR provided weak evidence for a causal effect of CRP on any anxiety disorder.Genetic and non-genetic analyses provided consistent evidence for an association, albeit small, of CRP on negative affect.Genetic analyses suggest that IL-6 signaling could be relevant for memory, and that the association between CRP and anxiety disorders could be potentially causal.

Affect
In ammation has generally been associated with higher levels of negative affect and lower levels of positive affect, although ndings are primarily based on medical populations [72][73][74] and small community samples 13,75 .To our knowledge, this is the rst large, population-based study to nd small but consistent associations of higher CRP with higher negative affect and lower positive affect, both unadjusted and adjusted for potential confounds.Interestingly, both CRP and GlycA genetic risk scores were associated with higher levels of negative affect, but not positive affect.This consistent association across nongenetic and genetic analyses may re ect the effect of in ammation on a range of emotional states beyond the cardinal features of depression (i.e., sadness/anhedonia), which aligns with prior research linking in ammation with fear and irritability 51,76 .Prior work has shown that in ammation is differentially associated with a speci c clinical presentation characterized by anhedonia and somatic/neurovegetative symptoms (e.g., fatigue, altered sleep and appetite changes) and further work is needed that more accurately characterize an in ammatory phenotype in depression 77,78 .

Depression
These data add to a growing body of evidence evaluating the role of in ammation in the etiology of depression.The results of non-genetic analyses broadly aligns with results from the UK Biobank cohort in terms of (i) prevalence estimates of depression and anxiety, (ii) robust univariate associations between CRP and depression and anxiety, which were generally no longer statistically signi cant when controlling for covariates, and (iii) stronger univariate associations for CRP and depression when compared to anxiety 37 .It has long been noted that variables being conceptualized as confounds that require statistical adjustment (e.g., BMI, medical illness) may, in fact, be key mechanisms in the pathophysiology of in ammatory depression 79,80 .As such, attenuation of associations following adjustment for covariates would not, by itself, indicate a non-causal relationship.Indeed, in ammation may increase risk for depression via increasing the risk of in ammation-related physical multimorbidity (e.g., cardiovascular disease) 34 -a hypothesis that requires further investigation.
In genetic analyses, there was also little evidence of an association between CRP GRS and depression outcomes, although there was evidence suggesting GlycA GRS increases liability to MDD.The null CRP ndings are consistent with previous MR studies showing no evidence of effect in MDD 42,43,81 .However, the MR literature of CRP on depression is mixed with some studies reporting CRP to decrease 37 or increase 34 risk for depression.It is unclear what accounts for these mixed ndings, but potential factors may include CRP SNP selection, de nition and/or measurement of depression, statistical power, and selection bias (see Supplementary Discussion).In contrast, MR studies have shown more consistent ndings for the potential causal role of IL-6 on depression [35][36][37]82 . Thisis similar to MR ndings for coronary heart disease, where IL-6 but not CRP have been shown to play a potential causal role 83,84 .
Consequently, studies on a broader range of immune markers (e.g.cytokines, immune cells) and speci c immune pathways would be more useful to understand the role of in ammation in depression, rather than CRP which is a non-speci c marker of systemic immune activation 85 .

Cognition
We observed relatively small effects of CRP on cognitive task performance, and in genetic analysis only sIL-6R GRS was associated with poor memory performance.Our ndings contribute to inconsistent ndings across population-based cohorts assessing circulating in ammatory biomarkers and cognitive performance where associations observed in population-based studies 25 often are not large in magnitude or consistently observed 44 .Few MR studies have been conducted on the role of in ammation on cognition.Consistent with results presented here, our previous MR study examining the role of the same in ammatory markers (i.e., CRP, IL-6, IL-6R, sIL-6R, GlycA) on speci c executive functions within the ALSPAC cohort (e.g., emotion recognition, working memory, response inhibition 44 ) found little evidence of a potential causal effect.However, Pagoni et al. recently reported that other cytokines and chemokines (i.e., Eotaxin, IL-8, MCP1) may be causally related to lower uid intelligence (and IL-4 with higher uid intelligence) 45 .The nding regarding sIL-6R and memory performance is novel and would align with convergent evidence that trans-signaling -in which sIL-6R plays a critical role -may be responsible for the deleterious effect of IL-6 on cognitive functioning 38,86 .
Interpreting the relationship between in ammation and cognitive task performance in population-based studies is di cult for several reasons.First, there is considerable heterogeneity in the cognitive abilities assessed across studies and there is a need for future studies to more uniformly include well-validated measures assessing individual differences, rather than detecting pathological states (e.g., dementia, epilepsy)] 87 .There a similar need to evaluate the impact that in ammation has on other psychological functions that impact cognition (e.g., reward process, aversive process) -there is strong theoretical work and empirical data to support an indirect effect of in ammation on cognition via, for instance, dysregulated reward circuitry that impact performance on cognitive tasks via decreased motivation or increased fatigue 88 .Moreover, there are a range of sociodemographic factors that may moderate the association between in ammation and cognition -prior work has found that in ammation and cognition may differ based on age and sex 89,90 .It is reasonable to assume, for instance, that modest increases in in ammation may exert a cumulative effect across the lifespan, and thus may only be detected later in life and/or in speci c domains of cognition.

Anxiety
In the non-genetic analyses, circulating CRP levels was associated with a modestly increased likelihood of meeting criteria for anxiety disorders, although this association was substantially attenuated following adjustments for covariates.Prior research in population-based cohorts have found CRP to associated with an increased risk for anxiety disorders 91,92 , although results are inconsistent and other studies indicate that anxiety prospectively predicts an increase in circulating CRP levels 93 .The MR analysis suggests a potential causal role of CRP on any anxiety disorder (which covers a broad range of anxiety-related conditions including panic disorder, social phobia, agoraphobia, GAD).Prior theory has primarily focused on anxiety as a cause of in ammation 94 ; however, alternative theories suggest that in ammatory physiology is implicated in both sickness behaviors (e.g., anhedonia, social withdrawal) and anxiety arousal and alarm 95 , which would align with the results presented here.

Limitations
This study used a large and population-based sample that employed triangulation of genetic and non-genetic analyses, which increases con dence in the inferences drawn.Limitations, however, exist.First, although broadly representative, like other cohort studies (e.g., UK Biobank), the Lifelines cohort predominantly includes individuals of European descent and is less representative of individuals from low socioeconomic status 96 , which consequentially limits the generalizability of ndings.Second, analyses were not corrected for multiple comparisons and are in need of replication.Moreover, as effect sizes reported are small and re ect associations in the general population, there is a need for studies to investigate whether there are sub-groups for whom these associations may be larger (e.g., older age, clinical populations).Third, in the genetic analysis the CRP GRS explained 1-4% of the variance in CRP [a level of variance consistent with similar analyses in the ALSPAC cohort 44 ] and few cases of depression were observed in Lifelines [although the point prevalence of approximately 4% is consistent with reported population point prevalence estimates 97 ].It is possible that this limited our capacity to detect potential causal effects, were they small in magnitude or non-linear.Fourth, the CogState tasks used in the current study may not be optimal for detecting individual differences in healthy individuals, or even in some conditions such as depression; multiple studies have shown that the CogState tasks used in this study do not improve in successful antidepressant trials, even when improvement in other cognitive measures are observed [98][99][100] .Fifth, although we include multiple instruments related to IL-6 (i.e., genetic variants related to IL-6 and sIL-6R levels), most instruments contain few genetic variants (≤ 3 SNPs) and genetic variants for IL-6 and sIL-6R overlap.While sIL-6R is involved in IL-6 trans-signaling, the overlap of SNPs makes it challenging to interpret the effect of these genetic variants on different immune phenotypes speci cally (i.e., IL-6 levels vs IL-6 signaling).Future studies are needed to better understand the biological role of these genetic variants and develop instruments proxying speci c IL-6 signaling pathways including IL-6 trans-signaling.Finally, it is worth considering that some instruments in the genetic analyses were associated with potential confounds.

Conclusions
Genetic and non-genetic analyses provide consistent evidence for a modest effect of CRP on negative affect.Genetic analyses suggest that IL-6 signaling could be relevant for memory, and that the association between CRP and anxiety disorders could be causal.Overall, these results suggest that in ammation may affect a range of emotional states beyond the cardinal features of depression.However, given the small effect sizes and multiple tests conducted, future studies should investigate whether effects are moderated by sub-groups and whether these ndings replicate in other cohorts.

Declarations Figures
Page 15/  Associations of genetic risk scores for in ammatory markers with mood, anxiety disorders and positive and negative affect scores.
Associations of genetic risk scores for in ammatory markers with cognitive task performance.

Table 1
Lifelines Cohort Sample Characteristics at Baseline and First Follow-up Assessment a = P > .05;B = higher values equal better performance; C = higher values equal poorer performance; T1 = Time 1 (Baseline); T2 = Time 2 (First Foll Fluency Test; Health Status.= Number of Medical Conditions Reported; for values ≤ .001and ≥-.001, values were rounded to 0.

Table 3
Associations of CRP levels with affect, depressive and anxiety disorders, and cognitive task performance in the Lifelines cohortPlease note: point estimates 95% con dence intervals (and N, p-value) as we do not currently have access to the Lifelines Cohort Workspace, the Cohort will allow us access to data for reviewer comments, we WILL add these (p-value, 95% CI, N) as necessary during review.